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Unverified Commit bec90472 authored by Aziz's avatar Aziz Committed by GitHub
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Redactor librosa compatibility test (#1259)

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test/torchaudio_unittest/functional/librosa_compatibility_test.py 0 → 100644
View file @ bec90472
import itertools
import unittest
from distutils.version import StrictVersion
import torch
from parameterized import parameterized
import torchaudio.functional as F
from torchaudio._internal.module_utils import is_module_available
LIBROSA_AVAILABLE = is_module_available('librosa')
if LIBROSA_AVAILABLE:
import numpy as np
import librosa
from torchaudio_unittest import common_utils
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestFunctional(common_utils.TorchaudioTestCase):
"""Test suite for functions in `functional` module."""
def test_griffinlim(self):
# NOTE: This test is flaky without a fixed random seed
# See https://github.com/pytorch/audio/issues/382
torch.random.manual_seed(42)
tensor = torch.rand((1, 1000))
n_fft = 400
ws = 400
hop = 100
window = torch.hann_window(ws)
normalize = False
momentum = 0.99
n_iter = 8
length = 1000
rand_init = False
init = 'random' if rand_init else None
specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt()
ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize,
n_iter, momentum, length, rand_init)
lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop,
momentum=momentum, init=init, length=length)
lr_out = torch.from_numpy(lr_out).unsqueeze(0)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None):
librosa_fb = librosa.filters.mel(sr=sample_rate,
n_fft=n_fft,
n_mels=n_mels,
fmax=fmax,
fmin=fmin,
htk=True,
norm=norm)
fb = F.create_fb_matrix(sample_rate=sample_rate,
n_mels=n_mels,
f_max=fmax,
f_min=fmin,
n_freqs=(n_fft // 2 + 1),
norm=norm)
for i_mel_bank in range(n_mels):
self.assertEqual(
fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5)
def test_create_fb(self):
self._test_create_fb()
self._test_create_fb(n_mels=128, sample_rate=44100)
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0)
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0)
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0)
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0)
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0)
if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"):
return
self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney")
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney")
def test_amplitude_to_DB(self):
spec = torch.rand((6, 201))
amin = 1e-10
db_multiplier = 0.0
top_db = 80.0
# Power to DB
multiplier = 10.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.power_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
# Amplitude to DB
multiplier = 20.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.amplitude_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestPhaseVocoder(common_utils.TorchaudioTestCase):
@parameterized.expand(list(itertools.product(
[(2, 1025, 400, 2)],
[0.5, 1.01, 1.3],
[256]
)))
def test_phase_vocoder(self, shape, rate, hop_length):
# Due to cummulative sum, numerical error in using torch.float32 will
# result in bottom right values of the stretched sectrogram to not
# match with librosa.
torch.random.manual_seed(42)
complex_specgrams = torch.randn(*shape)
complex_specgrams = complex_specgrams.type(torch.float64)
phase_advance = torch.linspace(
0,
np.pi * hop_length,
complex_specgrams.shape[-3],
dtype=torch.float64)[..., None]
complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance)
# == Test shape
expected_size = list(complex_specgrams.size())
expected_size[-2] = int(np.ceil(expected_size[-2] / rate))
assert complex_specgrams.dim() == complex_specgrams_stretch.dim()
assert complex_specgrams_stretch.size() == torch.Size(expected_size)
# == Test values
index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3
mono_complex_specgram = complex_specgrams[index].numpy()
mono_complex_specgram = mono_complex_specgram[..., 0] + \
mono_complex_specgram[..., 1] * 1j
expected_complex_stretch = librosa.phase_vocoder(
mono_complex_specgram,
rate=rate,
hop_length=hop_length)
complex_stretch = complex_specgrams_stretch[index].numpy()
complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1]
self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5)
test/torchaudio_unittest/librosa_compatibility_test.py
View file @ bec90472
"""Test suites for numerical compatibility with librosa"""
import os
import unittest
from distutils.version import StrictVersion
import torch
import torchaudio
import torchaudio.functional as F
from torchaudio._internal.module_utils import is_module_available
from parameterized import parameterized, param
import itertools
LIBROSA_AVAILABLE = is_module_available('librosa')
if LIBROSA_AVAILABLE:
import numpy as np
import librosa
import scipy
from torchaudio_unittest import common_utils
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestFunctional(common_utils.TorchaudioTestCase):
"""Test suite for functions in `functional` module."""
def test_griffinlim(self):
# NOTE: This test is flaky without a fixed random seed
# See https://github.com/pytorch/audio/issues/382
torch.random.manual_seed(42)
tensor = torch.rand((1, 1000))
n_fft = 400
ws = 400
hop = 100
window = torch.hann_window(ws)
normalize = False
momentum = 0.99
n_iter = 8
length = 1000
rand_init = False
init = 'random' if rand_init else None
specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt()
ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize,
n_iter, momentum, length, rand_init)
lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop,
momentum=momentum, init=init, length=length)
lr_out = torch.from_numpy(lr_out).unsqueeze(0)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
def _test_create_fb(self, n_mels=40, sample_rate=22050, n_fft=2048, fmin=0.0, fmax=8000.0, norm=None):
librosa_fb = librosa.filters.mel(sr=sample_rate,
n_fft=n_fft,
n_mels=n_mels,
fmax=fmax,
fmin=fmin,
htk=True,
norm=norm)
fb = F.create_fb_matrix(sample_rate=sample_rate,
n_mels=n_mels,
f_max=fmax,
f_min=fmin,
n_freqs=(n_fft // 2 + 1),
norm=norm)
for i_mel_bank in range(n_mels):
self.assertEqual(
fb[:, i_mel_bank], torch.tensor(librosa_fb[i_mel_bank]), atol=1e-4, rtol=1e-5)
def test_create_fb(self):
self._test_create_fb()
self._test_create_fb(n_mels=128, sample_rate=44100)
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0)
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0)
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0)
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0)
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0)
if StrictVersion(librosa.__version__) < StrictVersion("0.7.2"):
return
self._test_create_fb(n_mels=128, sample_rate=44100, norm="slaney")
self._test_create_fb(n_mels=128, fmin=2000.0, fmax=5000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=100.0, fmax=9000.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=800.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=56, fmin=1900.0, fmax=900.0, norm="slaney")
self._test_create_fb(n_mels=10, fmin=1900.0, fmax=900.0, norm="slaney")
def test_amplitude_to_DB(self):
spec = torch.rand((6, 201))
amin = 1e-10
db_multiplier = 0.0
top_db = 80.0
# Power to DB
multiplier = 10.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.power_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
# Amplitude to DB
multiplier = 20.0
ta_out = F.amplitude_to_DB(spec, multiplier, amin, db_multiplier, top_db)
lr_out = librosa.core.amplitude_to_db(spec.numpy())
lr_out = torch.from_numpy(lr_out)
self.assertEqual(ta_out, lr_out, atol=5e-5, rtol=1e-5)
@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
class TestPhaseVocoder(common_utils.TorchaudioTestCase):
@parameterized.expand(list(itertools.product(
[(2, 1025, 400, 2)],
[0.5, 1.01, 1.3],
[256]
)))
def test_phase_vocoder(self, shape, rate, hop_length):
# Due to cummulative sum, numerical error in using torch.float32 will
# result in bottom right values of the stretched sectrogram to not
# match with librosa.
torch.random.manual_seed(42)
complex_specgrams = torch.randn(*shape)
complex_specgrams = complex_specgrams.type(torch.float64)
phase_advance = torch.linspace(
0,
np.pi * hop_length,
complex_specgrams.shape[-3],
dtype=torch.float64)[..., None]
complex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance)
# == Test shape
expected_size = list(complex_specgrams.size())
expected_size[-2] = int(np.ceil(expected_size[-2] / rate))
assert complex_specgrams.dim() == complex_specgrams_stretch.dim()
assert complex_specgrams_stretch.size() == torch.Size(expected_size)
# == Test values
index = [0] * (complex_specgrams.dim() - 3) + [slice(None)] * 3
mono_complex_specgram = complex_specgrams[index].numpy()
mono_complex_specgram = mono_complex_specgram[..., 0] + \
mono_complex_specgram[..., 1] * 1j
expected_complex_stretch = librosa.phase_vocoder(
mono_complex_specgram,
rate=rate,
hop_length=hop_length)
complex_stretch = complex_specgrams_stretch[index].numpy()
complex_stretch = complex_stretch[..., 0] + 1j * complex_stretch[..., 1]
self.assertEqual(complex_stretch, torch.from_numpy(expected_complex_stretch), atol=1e-5, rtol=1e-5)
def _load_audio_asset(*asset_paths, **kwargs):
file_path = common_utils.get_asset_path(*asset_paths)
sound, sample_rate = torchaudio.load(file_path, **kwargs)
......
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